Update app.py

app.py

@@ -6,21 +6,6 @@ import streamlit as st
 import torch
 import requests
 
-#def prettier(results):
-#    for item in results:
-#        score = round(item['score'], 3)
- #       label = item['label']  # Use square brackets to access the 'label' key
-  #      location = [round(value, 2) for value in item['box'].values()]
-   #     print(f'Detected {label} with confidence {score} at location {location}')
-
-#def prettify_results(results):
- #   for item in results:
-  #      score = round(item['score'].item(), 3)
-   #     label = model.config.id2label[item['label']]  # Get label from id2label mapping in model config
-    #    box = [round(coord, 2) for coord in item['box']]
-     #   st.write(f'Detected {label} with confidence {score} at location {box}')
-# Function to process uploaded image and prepare input for model
-
 def input_image_setup(uploaded_file):
     if uploaded_file is not None:
         bytes_data = uploaded_file.getvalue()
@@ -29,7 +14,6 @@ def input_image_setup(uploaded_file):
     else:
         raise FileNotFoundError("No file uploaded")
 
-
 #Streamlit App
 st.set_page_config(page_title="Image Detection")
 st.header("Object Detection Application")
@@ -45,27 +29,6 @@ if uploaded_file is not None:
     image = Image.open(uploaded_file)
     st.image(image, caption="Uploaded Image.", use_column_width=True)
 submit = st.button("Detect Objects ")
-"""if submit:
-    image_data=input_image_setup(uploaded_file)
-    st.subheader("The response is..")
-    #process with model
-    inputs = processor(images=image, return_tensors="pt")
-    outputs = model(**inputs)
-
-    # model predicts bounding boxes and corresponding COCO classes
-    logits = outputs.logits
-    bboxes = outputs.pred_boxes
-    # print results
-    target_sizes = torch.tensor([image.size[::-1]])
-    results = processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[0]
-  #  prettify_results(results)
-    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
-        box = [round(i, 2) for i in box.tolist()]
-        st.write(
-            f"Detected {model.config.id2label[label.item()]} with confidence "
-            f"{round(score.item(), 3)} at location {box}"
-        )
-"""
 if submit:
     image_data = input_image_setup(uploaded_file)
     st.subheader("The response is..")
@@ -87,4 +50,10 @@ if submit:
         label_text = f"{model.config.id2label[label.item()]} ({round(score.item(), 2)})"
         draw.text((box[0], box[1]), label_text, fill="red")
     
-    st.image(drawn_image, caption="Detected Objects", use_column_width=True)
+    st.image(drawn_image, caption="Detected Objects", use_column_width=True)
+    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+        box = [round(i, 2) for i in box.tolist()]
+        st.write(
+            f"Detected {model.config.id2label[label.item()]} with confidence "
+            f"{round(score.item(), 3)} at location {box}"
+        )